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Tier-1 brand, mid-level generalist role, metro location, and broad skillset drive high competition.
Strong ML/cloud skills transferable, but consumer-goods marketing context increases domain specificity.
Explicit years, required ML stack, cloud and DevOps familiarity make filtering strict.
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Design and deploy scalable data science algorithms focused on optimization, simulation, and advanced machine learning including Generative and Agentic AI.
Collaborate with Data and AI Engineering teams to productionize machine learning models and handle large datasets in cloud environments.
Mentor team members as a technical coach and become recognized as a subject matter expert in data science techniques.
2-4 years of relevant work experience in data science or related quantitative field.
Bachelor's, Master's, or postgraduate degree in quantitative disciplines such as Operations Research, Computer Science, Engineering, Applied Mathematics, Statistics, or Analytics.
Proficiency in Python and familiarity with ML libraries like OpenCV, scikit-learn, PyTorch, TensorFlow/Keras, and Pandas.
Experience working with cloud computing platforms (e.g., GCP or Azure) and ability to develop/test code in such environments.
Experienced in applying a range of analytic methodologies including machine learning, optimization, simulation, and emerging AI techniques to solve real-world business problems.
Capable of end-to-end ownership from problem understanding with domain experts to designing solutions and collaborating for production deployment.
Comfortable in cloud environments with practical knowledge of DevOps principles, CI/CD tools, and large-scale data handling.